Entertainment Computing最新文献_第9页

“You Want to Play a Game?” Detecting Two Personality Traits with Short-Duration Mobile Games “你想玩游戏吗？”用短时间手机游戏检测两种人格特征

IF 2.4 3区计算机科学

Entertainment Computing Pub Date : 2025-09-01 Epub Date: 2025-09-15 DOI: 10.1016/j.entcom.2025.101020

Patrícia Alves , João Trindade , Gonçalo Monteiro , Pedro Campos , Pedro Saraiva , Goreti Marreiros , Paulo Novais

{"title":"“You Want to Play a Game?” Detecting Two Personality Traits with Short-Duration Mobile Games","authors":"Patrícia Alves , João Trindade , Gonçalo Monteiro , Pedro Campos , Pedro Saraiva , Goreti Marreiros , Paulo Novais","doi":"10.1016/j.entcom.2025.101020","DOIUrl":"10.1016/j.entcom.2025.101020","url":null,"abstract":"<div><div>Accurately determining someone’s personality is complex and often requires lengthy questionnaires, which are subject to social desirability bias, or a great amount of users’ interactions with the system. Also, most existing research focuses on broader personality dimensions rather than more granular personality traits, which better characterize a person.</div><div>In this work, we propose to implicitly acquire the users’ granular personality traits using mobile short-duration serious games, in < 5 min and in a single play interaction, namely cautiousness and achievement-striving as concept proof, to replace personality questionnaires.</div><div>Two platform mobile games were developed, one for each trait, Which Way and Time Travel, respectively. Then, an experiment with real participants (n = 100) was conducted. Time Travel proved to be capable of detecting achievers (get all coins, diamonds, and better scores), while Which Way couldn’t effectively measure cautiousness, although following hard paths could be related to less cautious persons. As expected, significant correlations with other personality traits were also found (15 out of 30), such as anger, modesty, excitement seeking, and adventurousness. Contrary to other types of (serious) games, the results show short-duration mobile minigames are a viable way of unobtrusively determining the users’ granular personality, being the path to replacing personality questionnaires.</div></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":"55 ","pages":"Article 101020"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Verbal harassment detection in online games using machine learning methods 利用机器学习方法检测网络游戏中的言语骚扰

IF 2.4 3区计算机科学

Entertainment Computing Pub Date : 2025-09-01 Epub Date: 2025-08-25 DOI: 10.1016/j.entcom.2025.101009

Helmi Hibatullah , Tuğçe Ballı , E. Fatih Yetkin

引用次数: 0

Impact of social media on the psychological well-being of rural Youth- A systematic literature review and bibliometric analysis 社交媒体对农村青年心理健康的影响——系统文献综述与文献计量分析

IF 2.4 3区计算机科学

Entertainment Computing Pub Date : 2025-09-01 Epub Date: 2025-08-19 DOI: 10.1016/j.entcom.2025.101012

M. Anshida , P.P. Murugan , M. Senthilkumar , M. Chandrakumar , G. Vanitha

{"title":"Impact of social media on the psychological well-being of rural Youth- A systematic literature review and bibliometric analysis","authors":"M. Anshida , P.P. Murugan , M. Senthilkumar , M. Chandrakumar , G. Vanitha","doi":"10.1016/j.entcom.2025.101012","DOIUrl":"10.1016/j.entcom.2025.101012","url":null,"abstract":"<div><div>The rise of social media has profoundly influenced the mental health of young people, including rural youth who face unique socio-economic challenges. This systematic literature review and bibliometric analysis investigates the impact of social media on the psychological well-being of rural youth, examining both positive and negative aspects. Social media fosters social connectivity and educational access but also contributes to anxiety, depression and low self-esteem through social comparison and cyberbullying. A systematic search of peer-reviewed articles from 2019 to May 2024 was conducted using the Scopus database. Inclusion criteria focused on studies addressing the psychological effects of social media on rural youth. Data were analyzed using Bibliometrics, an R package for bibliometric analysis, to identify trends, collaborations and key themes. The findings highlight the dual nature of social media’s impact. While it provides opportunities for interaction and access to resources, excessive or maladaptive use, particularly exposure to idealized content and cyberbullying, poses significant mental health risks. Rural youth are especially vulnerable due to isolation and limited access to mental health support. Understanding this nuanced role is vital for designing interventions that maximize social media’s benefits while mitigating risks, promoting the psychological well-being of rural youth in the digital age.</div></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":"55 ","pages":"Article 101012"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The expression method of Chinese creative elements in animation films based on artificial intelligence technology 基于人工智能技术的中国创意元素在动画电影中的表现方法

IF 2.4 3区计算机科学

Entertainment Computing Pub Date : 2025-09-01 Epub Date: 2025-08-25 DOI: 10.1016/j.entcom.2025.101015

Fengtian Shao

{"title":"The expression method of Chinese creative elements in animation films based on artificial intelligence technology","authors":"Fengtian Shao","doi":"10.1016/j.entcom.2025.101015","DOIUrl":"10.1016/j.entcom.2025.101015","url":null,"abstract":"<div><div>This study explores the application of artificial intelligence to enhance the representation and evaluation of Chinese cultural elements in animated films, emphasizing both cultural significance and market potential while redefining intellectual property (IP) value in the industry. A major challenge addressed is the accurate assessment of cultural content, as traditional Back Propagation Neural Networks (BPNNs) often suffer from slow convergence and local minima issues. To overcome these limitations, the research proposes an improved GA-BP model, combining BPNN’s localized optimization with the global search capabilities of Genetic Algorithms (GA). The paper reviews cultural development theories and examines the status of Chinese and international animation IPs. Experimental results show that the GA-BP model achieves higher accuracy and stability than standard BPNNs, closely matching expert evaluations. This validates its effectiveness in supporting intelligent cultural evaluation and creative design in animation. By applying AI techniques to cultural evaluation, the research applies artificial intelligence methods to evaluate and support the structured integration of cultural elements into animated film design, laying a methodological groundwork for innovation in Chinese animated films. It supports cultural sustainability and strengthens national cultural identity through digital storytelling, contributing to both academic inquiry and industry practice.</div></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":"55 ","pages":"Article 101015"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting film audience ratings: A natural language processing approach to script and production data 预测电影收视率：对剧本和制作数据的自然语言处理方法

IF 2.4 3区计算机科学

Entertainment Computing Pub Date : 2025-09-01 Epub Date: 2025-10-21 DOI: 10.1016/j.entcom.2025.101043

Karl Cini , John Abela

{"title":"Forecasting film audience ratings: A natural language processing approach to script and production data","authors":"Karl Cini , John Abela","doi":"10.1016/j.entcom.2025.101043","DOIUrl":"10.1016/j.entcom.2025.101043","url":null,"abstract":"<div><div>The film industry is an important entertainment avenue for audiences of all ages. Demand for good quality scripts remains a core element of this industry, rendering the screenplay a pivotal tool at the green lighting stage.</div><div>While previous work addressed isolated elements influencing the performance of a movie, this research aims to bring together known influential factors and some novel approaches by applying Natural Language Processing (NLP) and Machine Learning (ML) techniques to analyse movie scripts, with the aim of extracting valuable insights and patterns that are able to predict the audience rating as collated by the Internet Movie Database (IMDb).</div><div>This research helps producers determine which movies are most viable for financing. By providing a sound method to sift through and rank the various script projects presented to them, they can focus on scripts that are likely to perform better.</div><div>Methods adopted in this research include the use of lexicons for the extraction of linguistic features, the analysis of emotional arcs in movies, embedding strategies for the script and statistical features generated from sentiment analysis. These features are concatenated to cast and crew specific factors to train various regression models by using a forward rolling window training strategy.</div></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":"55 ","pages":"Article 101043"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating artificial intelligence and gamification in rehabilitation: A scoping review 整合人工智能和游戏化在康复：范围审查

IF 2.4 3区计算机科学

Entertainment Computing Pub Date : 2025-09-01 Epub Date: 2025-08-13 DOI: 10.1016/j.entcom.2025.101011

Minkai Wang , Jingdong Zhu , Wei Qian , Hanjie Gu

{"title":"Integrating artificial intelligence and gamification in rehabilitation: A scoping review","authors":"Minkai Wang , Jingdong Zhu , Wei Qian , Hanjie Gu","doi":"10.1016/j.entcom.2025.101011","DOIUrl":"10.1016/j.entcom.2025.101011","url":null,"abstract":"<div><div>The integration of artificial intelligence (AI) and gamification represents a promising direction in rehabilitation, enabling personalized training, real-time feedback, and enhanced patient engagement. However, research in this area remains fragmented, necessitating a structured synthesis. This scoping review analyzed 20 studies published over the past two decades and indexed in PubMed, Cochrane Library, Web of Science, and IEEE Xplore. Stroke rehabilitation was the most frequently addressed condition (9 studies), followed by motor dysfunction (2), rheumatoid arthritis (2), cognitive impairment (3), phantom limb pain (2), ADHD (1), and multiple sclerosis (1). AI techniques included machine learning (10 studies), deep learning (4), and unspecified methods (6), while game-based interventions were categorized as gamification (4), serious games (8), and digital games (8). Reported outcomes indicated improvements in motivation, therapy adherence, and various metrics of rehabilitation performance. Nonetheless, key challenges persist, including small sample sizes, methodological heterogeneity, data privacy concerns, and the lack of large-scale clinical trials. Barriers related to cost, accessibility, and ethics also hinder broader implementation. Future research should prioritize multimodal data integration, AI-driven remote rehabilitation platforms, and wearable technologies to enhance scalability and clinical impact.</div></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":"55 ","pages":"Article 101011"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lock the look: Recommending trendy looks for fashion products using natural language processing 锁定外观：使用自然语言处理为时尚产品推荐时尚外观

IF 2.4 3区计算机科学

Entertainment Computing Pub Date : 2025-09-01 Epub Date: 2025-11-01 DOI: 10.1016/j.entcom.2025.101049

Manjarini Mallik , Tushti Thakur , Chandreyee Chowdhury

{"title":"Lock the look: Recommending trendy looks for fashion products using natural language processing","authors":"Manjarini Mallik , Tushti Thakur , Chandreyee Chowdhury","doi":"10.1016/j.entcom.2025.101049","DOIUrl":"10.1016/j.entcom.2025.101049","url":null,"abstract":"<div><div>The recreation of looks established by favorite movie characters or fashion icons is a popular trend in this decade. It is difficult to find out the dresses and accessories required to develop that look as current product recommendations are mostly based on history of users’ choices. There exists computer vision-based solutions that check image-wise similarities between the desired looks and available fashion products from e-commerce stores. However, this is a resource hungry complex process as plenty of product images would be analyzed. In this work an NLP-based lightweight look recommendation system is proposed. In the proposed approach, multiple text descriptions of trendy looks are collected from different websites to build the training dataset. A subset of two benchmark datasets (Myntra Products Dataset and Ajio Products Dataset) have been used for recommendation. Using the bag of words technique, text datasets are embedded, and a set of looks is recommended for each product. The system is validated using Cosine similarity and Cohen’s kappa metrics. Products in the test dataset have been mapped to their 1st and 2nd highest recommended looks with positive scores. We observed a minimum score of 0.6 and 0.2 for Cosine similarity and Cohen’s kappa respectively, representing appreciable performance.</div></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":"55 ","pages":"Article 101049"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking reinforcement learning algorithms in first-person shooter games using VizDoom 使用VizDoom测试第一人称射击游戏中的强化学习算法

IF 2.4 3区计算机科学

Entertainment Computing Pub Date : 2025-09-01 Epub Date: 2025-10-04 DOI: 10.1016/j.entcom.2025.101031

Adil Khan , Aamir Aqeel

{"title":"Benchmarking reinforcement learning algorithms in first-person shooter games using VizDoom","authors":"Adil Khan , Aamir Aqeel","doi":"10.1016/j.entcom.2025.101031","DOIUrl":"10.1016/j.entcom.2025.101031","url":null,"abstract":"<div><div>Computer games are considered one of the best test beds for evaluating artificial intelligence algorithms, as it is a well-known practice before applying the algorithms in the real world, such as the robotics industry. A machine learning technique, known as reinforcement learning, utilizes positive and negative rewards to guide an artificial intelligence agent as it learns new tactics and strategies. This study compares four reinforcement learning algorithms: Dueling Double Deep Q-Network (Dueling DDQN), Advantage Actor-Critic (A2C), LSTM-Based Advantage Actor-Critic (A2C LSTM), and REINFORCE. The game artificial intelligence (Game AI) based platform VizDoom evaluates and compares these reinforcement learning algorithms. VizDoom is based on the first-person shooter (FPS) video game Doom, which has had a significant influence on artificial intelligence. The results are compared, and, in most cases, Dueling DDQN outperformed all other algorithms in all chosen scenarios. However, in contrast, the A2C performed well for the kills metric in the defending the center scenario only. Finally, the proposed work’s analysis, implications, and limitations are presented, along with the potential future directions for research.</div></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":"55 ","pages":"Article 101031"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can LLMs predict the success of Turkish TV series from their first episodes? 法学硕士能否从土耳其电视剧的第一集就预测其成功？

IF 2.4 3区计算机科学

Entertainment Computing Pub Date : 2025-09-01 Epub Date: 2025-11-12 DOI: 10.1016/j.entcom.2025.101052

Firat Ismailoglu

{"title":"Can LLMs predict the success of Turkish TV series from their first episodes?","authors":"Firat Ismailoglu","doi":"10.1016/j.entcom.2025.101052","DOIUrl":"10.1016/j.entcom.2025.101052","url":null,"abstract":"<div><div>Turkey is the third largest exporter of TV series worldwide. However, half of these series are cancelled early leading to economic and social consequences. In this study, we explore whether the success of these series can be predicted from the scripts of their first episodes using LLMs. We built a dataset of first-episode scripts from recently aired Turkish series and trained LLM-based models on it. The main challenge we faced is that these scripts are very long, making them unsuitable for standard BERT models. This led to one of the key contributions of our study, as there is currently no research that specifically focuses on handling long Turkish texts. We pretrained a BigBird model from scratch for Turkish and fine-tuned it for our task. We also developed a Hierarchical Attention Network (HAN) model capable of processing long Turkish texts. While predicting the exact number of episodes is difficult, both HAN and BigBird achieve strong performance in binary classification setup, distinguishing successful series from unsuccessful ones. Additionally, we investigate whether audience preferences in Turkey have changed over time by testing our models on some iconic older Turkish series to see if they would still be classified as successful by today’s standards.</div></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":"55 ","pages":"Article 101052"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling gender bias in LLM-generated hero and heroine narratives 揭示法学硕士生成的男女主人公叙事中的性别偏见

IF 2.8 3区计算机科学

Entertainment Computing Pub Date : 2025-09-01 Epub Date: 2025-06-16 DOI: 10.1016/j.entcom.2025.100972

Irene C.E. van Blerck , Edirlei Soares de Lima , Margot M.E. Neggers , Toon Calders

{"title":"Unveiling gender bias in LLM-generated hero and heroine narratives","authors":"Irene C.E. van Blerck , Edirlei Soares de Lima , Margot M.E. Neggers , Toon Calders","doi":"10.1016/j.entcom.2025.100972","DOIUrl":"10.1016/j.entcom.2025.100972","url":null,"abstract":"<div><div>This article investigates gender bias in narratives generated by Large Language Models (LLMs) through a two-phase study. Building on our existing work in narrative generation, we employ a structured methodology to analyze the influence of protagonist gender on both the generation and classification of fictional stories. In Phase 1, factual narratives were generated using six LLMs, guided by predefined narrative structures (Hero’s Journey and Heroine’s Journey). Gender bias was quantified through specialized metrics and statistical analyses, revealing significant disparities in protagonist gender distribution and associations with narrative archetypes. In Phase 2, counterfactual narratives were constructed by altering the protagonists’ genders while preserving all other narrative elements. These narratives were then classified by the same LLMs to assess how gender influences their interpretation of narrative structures. Results indicate that LLMs exhibit difficulty in disentangling the protagonist’s gender from the narrative structure, often using gender as a heuristic to classify stories. Male protagonists in emotionally driven narratives were frequently misclassified as following the Heroine’s Journey, while female protagonists in logic-driven conflicts were misclassified as adhering to the Hero’s Journey. These findings provide empirical evidence of embedded gender biases in LLM-generated narratives, highlighting the need for bias mitigation strategies in AI-driven storytelling to promote diversity and inclusivity in computational narrative generation.</div></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":"55 ","pages":"Article 100972"},"PeriodicalIF":2.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0